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Software analysis approaches

 

1. Introduction

Analysis is the foundation of software development, ensuring that a system meets business needs efficiently. Different types of analysis focus on various aspects of a system, including requirements, data, processes, and system behavior. This guide explores multiple analysis approaches, their applicability, techniques, models, and best use cases.

2. What is Analysis?

Analysis in IT refers to understanding, defining, and documenting how a system should function. It can be broken down into the following categories:

Requirement Analysis

📌 Focus: Capturing business needs and defining system expectations.

✅ Key Techniques:

  • Functional & Non-Functional Requirement Documentation

  • User Stories & Use Cases

  • Stakeholder Interviews

System Analysis

📌 Focus: Understanding system components, interactions, and constraints.

✅ Key Techniques:

  • System Modeling

  • Component Diagrams

  • Architecture Review

Data Analysis

📌 Focus: Structuring and organizing information for efficient storage and retrieval.

✅ Key Techniques:

  • Data Modeling (ERD, Normalization)

  • Data Mining & Analytics

  • Big Data Schema Design

Process Analysis

📌 Focus: Examining workflows and identifying inefficiencies.

✅ Key Techniques:

  • Business Process Modeling (BPMN)

  • Workflow Automation Analysis

  • Value Stream Mapping

3. Types of Analysis Approaches

Different analysis approaches suit different system types and methodologies. Below are the major types of analysis approaches:

1️⃣ Structured Analysis

✅ Applicability: Best for procedural and transactional systems with well-defined workflows. 

✅ Suitable System Types: Legacy systems, financial applications, traditional business software. 

✅ Common Techniques:

  • Data Flow Diagrams (DFD)

  • Entity-Relationship Diagrams (ERD)

  • State Transition Diagrams 

✅ Main Model: Process-oriented model focusing on system workflows and functions. 
✅ Best Suited For: Systems with clear process flows and minimal flexibility requirements. 
✅ Reusability: Limited due to tight coupling with specific workflows. 
✅ Scalability: Moderate; works well for smaller, structured systems but struggles with complex, dynamic requirements. 
📌 Example: Legacy banking systems using COBOL with DFD and ERD.

2️⃣ Object-Oriented Analysis (OOA)

✅ Applicability: Best for modular and reusable software architectures. 

✅ Suitable System Types: Modern applications, enterprise systems, web and mobile applications. 

✅ Common Techniques:

  • UML diagrams (Class, Sequence, Use Case)

  • Object Modeling 

✅ Main Model: Object-oriented model representing real-world entities and behaviors. 
✅ Focus: Objects, relationships, and interactions. 
✅ Best Suited For: Applications developed using object-oriented programming languages (Java, C++, Python). 
✅ Reusability: High, due to modular components and encapsulation. 
✅ Scalability: High; supports modularization, maintainability, and code reuse. 
📌 Example: Java-based CRM system using UML for modeling.

3️⃣ Functional Decomposition

✅ Applicability: Best for systems requiring a hierarchical breakdown of business functions. 

✅ Suitable System Types: Monolithic applications, business process automation, workflow-driven systems. 

✅ Common Techniques:

  • Hierarchical Function Breakdown

  • Process Modeling 

✅ Main Model & Focus: Breaking down system functionality into hierarchical, manageable components. 
✅ Best Suited For: Systems where business rules and functions need clear separation.
✅ Reusability: Moderate, as functions can sometimes be reused across systems. 
✅ Scalability: Moderate; can become complex if not well-structured. 
📌 Example: ERP modules like Finance, HR, and Sales.

4️⃣ Event-Driven Analysis

✅ Applicability: Best for systems that respond to user actions and real-time events. 

✅ Suitable System Types: Real-time systems, microservices, IoT, distributed systems. 

✅ Common Techniques:

  • Event Modeling

  • Event Storming

  • Finite State Machines 

✅ Main Model & Focus: Defining events and their consequences on system state. 

✅ Best Suited For: Asynchronous and reactive systems with dynamic event flows. 
✅ Reusability: High; decoupled event handling enables modular design. 
✅ Scalability: High; supports distributed and microservices architectures. 
📌 Example: Traffic light control systems using state transition diagrams.

5️⃣ Data-Centric Analysis

✅ Applicability: Best for data-heavy applications requiring structured storage and retrieval. 

✅ Suitable System Types: Data warehouses, analytics platforms, database systems.

 ✅ Common Techniques:

  • Entity-Relationship Modeling (ERM)

  • Data Normalization 

✅ Main Model & Focus: Prioritizing relationships, integrity, and structured data management. 
✅ Best Suited For: Systems where data consistency, retrieval, and analysis are critical. 
✅ Reusability: High; well-designed data models can be reused across multiple applications. 
✅ Scalability: High; supports large-scale data storage and distributed processing. 
📌 Example: CRM system with SQL database using ERD and normalization.

6️⃣ Domain-Driven Design (DDD)

✅ Applicability: Best for complex business applications with evolving requirements. 

✅ Suitable System Types: Enterprise applications, microservices, domain-centric applications. 

✅ Common Techniques:

  • Bounded Contexts

  • Aggregates

  • Domain Events 

✅ Main Model & Focus: Aligning system design with business domains and real-world processes. ✅ Best Suited For: Large-scale applications requiring strong business logic alignment. 
✅ Reusability: High; domain models and logic can be shared across microservices and teams. 
✅ Scalability: High; enables modularization and independent scaling of domain components. 
📌 Example: E-commerce platform using microservices for orders, payments, and inventory.

4. Choosing the Right Analysis Approach

ApproachBest ForScalability
Structured AnalysisLegacy transactional systemsModerate
Object-OrientedModern applications & enterprise systemsHigh
Functional Decomp.Monolithic business applicationsModerate
Event-DrivenReal-time and asynchronous event processingHigh
Data-CentricDatabase-heavy & analytical applicationsHigh
DDDBusiness-focused scalable applicationsHigh

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